Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression
The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the...
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2021
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oai:doaj.org-article:503e01cd0bf64ca581e915d68bba91d32021-11-11T18:56:22ZIdentifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression10.3390/rs132144282072-4292https://doaj.org/article/503e01cd0bf64ca581e915d68bba91d32021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4428https://doaj.org/toc/2072-4292The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.Lu NiuZhengfeng ZhangZhong PengYingzi LiangMeng LiuYazhen JiangJing WeiRonglin TangMDPI AGarticleSUHIMODISdriven factorspatial heterogeneityspatial scaleland useScienceQENRemote Sensing, Vol 13, Iss 4428, p 4428 (2021) |
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SUHI MODIS driven factor spatial heterogeneity spatial scale land use Science Q |
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SUHI MODIS driven factor spatial heterogeneity spatial scale land use Science Q Lu Niu Zhengfeng Zhang Zhong Peng Yingzi Liang Meng Liu Yazhen Jiang Jing Wei Ronglin Tang Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression |
| description |
The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers. |
| format |
article |
| author |
Lu Niu Zhengfeng Zhang Zhong Peng Yingzi Liang Meng Liu Yazhen Jiang Jing Wei Ronglin Tang |
| author_facet |
Lu Niu Zhengfeng Zhang Zhong Peng Yingzi Liang Meng Liu Yazhen Jiang Jing Wei Ronglin Tang |
| author_sort |
Lu Niu |
| title |
Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression |
| title_short |
Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression |
| title_full |
Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression |
| title_fullStr |
Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression |
| title_full_unstemmed |
Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression |
| title_sort |
identifying surface urban heat island drivers and their spatial heterogeneity in china’s 281 cities: an empirical study based on multiscale geographically weighted regression |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/503e01cd0bf64ca581e915d68bba91d3 |
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